Traditional methods for localization, such as ML [1]} and AMCL [2]} are “passive” (agnostic to how actions are selected). They provide a recursive framework for updating an approximation of the state belief posterior as new measurements arrive. In the case of ML, this is usually done by some type of discretization of the state space (i.e. a fixed grid) and in AMCL is achieved by maintaining a set of particles (state hypotheses). While these methods have seen widespread success in practice, they are still fundamentally limited since the map representation is hand-engineered and specifically tailored for the given on-board robot sensor. A common example is the pairing of the occupancy grid map [3]} with the laser scanner since the measurement likelihood can be computed efficiently and in closed form with scan matching [4]}. However, this choice of representation can be sub-optimal and inflexible. Furthermore, sensor parameters such as error covariances tend to be hand-tuned for performance, which is time consuming and error-inducing.
